model-tuning

An Interactive Approach to Understanding Deep Learning with Keras

skrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.

Awesome list of AutoML frameworks - curated by @oskar-j

Using Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.

Project with examples of different recommender systems created with the Surprise framework. Different algorithms (with a collaborative filtering approach) are explored, such as KNN or SVD.

Recommender systems with collaborative filtering created with Apache Mahout framework. The system uses a Music Recommendation dataset for research purposes as input, but you can train it and predict recommendations with any other dataset.

Over-fitting and model tuning

Apply supervised machine learning techniques and an analytical mind on data collected for the U.S. census to help CharityML (a fictitious charity organization) identify people most likely to donate to their cause

This Repository contains the projects which are part of Udacity Machine Learning Nanodegree

Udacity Data Scientist Nanodegree Project - Employ supervised algorithms to accurately model individuals income

پخش زنده پیرامون برخی اصطلاحات و مشکلات در یادگیری ماشین و یادگیری عمیق

I used this notebook to discuss different supervised learning approaches. In the notebook you can find evaluations of a logistic regression, a K-Nearest-Neighboor, a Support Vector Machine, a Decision Tree and the ensemble methods Random Forest, AdaBoost and XGBoost Classifyer.

This linear regression model tuning is an exercise for a Data Science Online Bootcamp.

A project featuring use of statistical techniques for exploratory data analysis and data mining techniques for predicting the quality of wine.

Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.

Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.

Machine learning prediction project, R studio, 2019.

In this project we will try to predict if the person has diabetes has or not.

Train a Smartcab to Drive

Predicting Boston Housing Prices

Applying Supervised learning techniques on data to help CharityML identify people most likely to donate to their cause.

Built an algorithm to identify canine breed given an image of a dog. If given image of a human, the algorithm identifies a resembling dog breed.

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

I built a Python application that trained an image classifier on an Oxford flower dataset to recognize different species of flowers, and then predicted new flower images using the trained model. This project is a starting point in the world of deep learning and neural networks, implemented here using Keras, TensorFlow and transfer learning techniques.

Applying AI to medical use cases: Diagnoses of lung and brain disorders, Building risk models and survival estimators for heart disease via RF, and Using NLP to extract information from radiology reports.